CAT: a computational anatomy toolbox for the analysis of structural MRI data

Abstract A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)—a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams—illustrated on an example dataset—allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Figure 1: Elements of the gr a phical user interface.The SPM menu (A) and CAT menu (B) allow access to the (C) SPM batch editor to control and combine a variety of functions.At the end of the processing stream, cross-sectional and longitudinal outputs are summarized in a brain-specific 1-pa ge r eport (D, E).In addition, C AT pro vides options to c hec k ima ge quality (F) and sample homogeneity (G) to allow outliers to be r emov ed befor e a ppl ying the final statistical analysis, including threshold-free cluster enhancement-TFCE (H); the numerical and graphical output can then be r etrie v ed (I), including surface projections (J).For beginners, there is an interactive help (K) as well as a user manual (L).For experts, command line tools (M) are available under Linux and MacOS.MATLAB license.It was originally designed to work with SPM12 [ 12 ] and is compatible with MATLAB versions 7.4 (R2007a) and later.No additional software or toolbox is r equir ed.The latest v ersion of CAT can be downloaded here: [ 9 ].The precompiled standalone version for Windows , Mac , or Linux operating systems can be downloaded here: [ 13 ].All steps necessary to install and run CAT are documented in the user manual [ 14 ] and in the complementary online help, which can be accessed directly via CAT's help functions .T he C AT softwar e is fr ee but copyrighted and distributed under the terms of the GNU General Public License, as published by the Free Software Foundation.
CAT can be started through SPM, from the MATLAB command window, from a shell, or as a standalone version.Except when called from the command shell (CAT is fully scriptable), a user interface will appear (see Fig. 1 ), allowing easy access to all analysis options and most additional functions.In addition, a gr a phical output window will display the inter activ e help to get started.This inter activ e help will be replaced by the results of the analyses (i.e., in that same window) but can always be called again via the user interface.

Computational morphometry
CAT's processing pipeline (see Fig. 2 ) contains 2 main streams: (i) voxel-based processing for voxel-based morphometry (VBM) and (ii) surface-based processing for surface-based morphometry (SBM).The former is a pr er equisite for the latter, but not the other way round.Both processing streams can be extended to include additional steps for (iii) region-based processing and region-based morphometry (RBM).

Voxel-based processing
Vo xel-based processing ste ps can be r oughl y divided into a module for tissue segmentation, follo w ed b y a module for spatial r egistr ation.
r Tissue Segmentation: The process is initiated by a ppl ying a spatially adaptive nonlocal means (SANLM) denoising filter [ 15 ], follo w ed b y SPM's standar d unified segmentation [ 16 ].The resulting output serves as a starting point for further optimizations and CAT's tissue segmentation steps: first, the brain is parcellated into the left and right hemispheres, subcortical ar eas, v entricles, and cer ebellum.In addition, local white matter hyperintensities are detected (to be later accounted for during the spatial r egistr ation and the optional surface processing).Second, a local intensity transformation is performed to reduce the effects of higher gray matter intensities in the motor cortex, basal ganglia, and occipital lobe, which are influenced by varying degrees of my elination.Thir d, an adaptive maximum a posteriori (AMAP) segmentation is applied, which does not require any a priori information on the tissue probabilities [ 17 ].The AMAP segmentation also includes a partial volume estimation [ 18 ].Figur e 3 A pr ovides information on the accuracy of CAT's tissue segmentation.
r Spatial Registration: Geodesic Shooting [ 24 ] is used to reg- ister the individual tissue segments to standardized templates in the ICBM 2009c Nonlinear Asymmetric space ( MNI152NLin2009cAsym [ 25 ]), her eafter r eferr ed to as MNI space.While MNI space is also used in many other software pac ka ges, enabling cr oss-study comparisons , users ma y also Downloaded from https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520 by B Pool-Zobel user on 07 August 2024  [ 15 ] and resampled to an isotropic voxel size.After applying an initial bias correction to facilitate the affine registration, SPM's unified segmentation [ 16 ] is used for the skull stripping and as a starting estimate for the ada ptiv e maxim um a posteriori (AMAP) segmentation [ 17 ] with partial volume estimation (PVE) [ 18 ].In addition, SPM's segmentation is used to locall y corr ect ima ge intensities.Finall y, the outcomes of the AMAP segmentation ar e r egister ed to the MNI template using SPM's shooting r egistr ation.The outcomes of the AMAP segmentation are also used to estimate cortical thickness and the central surface using a projection-based thickness (PBT) method [ 19 ].More specifically, after repairing topology defects [ 20 ], central, pial, and white matter surface meshes are generated.The individual left and right central surfaces are then registered to the corresponding hemisphere of the FreeSurfer template using a 2D version of the DARTEL approach [ 21 ].In the final step, the pial and white matter surfaces are used to refine the initial cortical thickness estimate using the FreeSurfer thickness metric [ 22 , 23 ].choose to use their own templates.Figure 3 B provides information on the accuracy of CAT's spatial registration.

Voxel-based morphometry (VBM)
VBM is applied to investigate the volume (or local amount) of a specific tissue compartment [ 16 , 26 ]-usually gray matter.VBM incor por ates differ ent pr ocessing steps: (i) tissue segmentation and (ii) spatial r egistr ation, as detailed abov e, and in addition, (iii) adjustments for volume changes due to the r egistr ation (modulation) as well as (iv) convolution with a 3-dimensional (3D) Gaussian kernel (spatial smoothing).As a side note, the modulation step results in voxel-wise gray matter volumes that are the same as in native space (i.e., before spatial registration) and not corrected for brain size yet.To remove effects of brain size, users Downloaded from https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520 by B Pool-Zobel user on 07 August 2024 Most a ppr oac hes for brain segmentation assume that each voxel belongs to a particular tissue class, such as gray matter (GM), white matter (WM), or cerebrospinal fluid (CSF).Ho w e v er, the spatial resolution of brain images is limited, leading to so-called partial volume effects (PVE) in voxels containing a mixture of different tissue types, such as GM/WM and GM/CSF.As PVE a ppr oac hes ar e highl y susceptible to noise, we combined the PVE model [ 18 ] with a spatial ada ptiv e nonlocal means denoising filter [ 15 ].To validate our method, we used a gr ound-truth ima ge fr om the Br ainWeb [ 31 ] database with varying noise le v els of 1-9%.The segmentation accur acy for all tissue types (GM, WM, CSF) was determined by calculating a kappa coefficient (a kappa coefficient of 1 means that there is perfect correspondence between the segmentation result and the ground truth).Left panel: The effect of the PVE model and the denoising filter on the tissue segmentation at the extremes of 1% and 9% noise.Right panel: The kappa coefficient over the range of different noise levels.Both panels demonstrate the advantage of combining the PVE model with a spatial ada ptiv e nonlocal means denoising filter, with particularly strong benefits for noisy data.(B) Registration Accuracy: To ensure an appropriate overlap of corresponding anatomical regions across brains, high-dimensional nonlinear spatial registration is r equir ed.CAT uses a sophisticated shooting a ppr oac h [ 24 ], together with an av er a ge template created from the IXI dataset [ 32 ].The figure shows the impr ov ed accur acy (i.e., a mor e detailed av er a ge ima ge) when spatiall y r egistering 555 br ains using the so-called shooting r egistr ation and the Dartel r egistr ation compar ed to the SPM standard r egistr ation.(C) Preprocessing Accuracy: We v alidated the performance of region-based morphometry (RBM) in CAT by comparing measures derived from automatically extracted regions of interest (ROI) versus manually labeled ROIs.For the voxel-based analysis, we used 56 structur es, manuall y labeled in 40 brains that provided the basis for the LPBA40 atlas [ 33 ].The gray matter volumes from those manually labeled regions served as the ground truth against which the gray matter volumes calculated using CAT and the LPBA40 atlas were then compared.For the surface-based analysis, we used 34 structures that were manually labeled in 39 brains according to Desikan et al. [ 34 ].The mean cortical thic kness fr om those manuall y labeled r egions serv ed as the gr ound truth a gainst whic h the mean cortical thic kness calculated using CAT and the Desikan atlas were compared.The diagrams show excellent overlap between manually and automatically labeled regions in both voxel-based (left) and surface-based (right) analyses.(D) Consistency of Segmentation and Surface Creation: Data from the same brain were acquired on MRI scanners with differ ent isotr opic spatial r esolutions and differ ent field str engths: 1.5T MPRA GE with a 1-mm voxel size, 3T MPRA GE with a 0.8-mm voxel size, and 7T MP2RAGE with a 0.7-mm voxel size.Section Views: The left hemispheres depict the central ( green ), pial ( blue ), and white matter ( red ) surfaces; the right hemispheres show the gray matter segments.Rendered Views: The color bar encodes point-wise cortical thickness projected onto the left hemisphere central surface.Both section views and hemisphere renderings demonstrate the consistency of the outcomes of the segmentation and surface cr eation pr ocedur es acr oss differ ent spatial r esolutions and field str engths.
Downloaded from https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520 by B Pool-Zobel user on 07 August 2024 Figure 4: Cortical Measurements: Surface-based morphometry is applied to investigate cortical surface features (i.e., cortical thickness and various parameters of cortical folding) at thousands of surface points.Cortical Thickness: One of the best-known and most frequently used morphometric measures is cortical thickness, which captures the width of the gray matter ribbon as the distance between its inner boundary (white matter surface) and outer boundary (pial surface).Cortical Folding: CAT provides distinct cortical folding measur es, deriv ed fr om the geometry of the centr al surface: "Gyrification" is calculated via the absolute mean curv atur e [ 35 ] of the central surface."Sulcal Depth" is calculated as the distance from the central surface to the enclosing hull [ 36 ]. "Cortical Complexity" is calculated using the fractal dimension of the central surface area from spherical harmonic r econstructions [ 37 ].Finall y, "Surface Ratio" is calculated as the ratio between the area of the central surface contained in a sphere of a defined size and that of a disk with the same radius [ 38 ].
have at least 2 options: (i) calculating the total intr acr anial volume (TIV) and including TIV as a covariate in the statistical model [ 27 ] or (ii) selecting "global scaling" (see second-le v el options in SPM).The latter is recommended if TIV is linked with (i.e., not orthogonal to) the effect of interest (e.g., sex), which can be tested (see "Design orthogonality" in SPM).

Surf ace-based pr ocessing
The optional surface-based processing comprises a series of steps that can be r oughl y divided into a module for surface creation, follo w ed b y a module for surface r egistr ation.
r Surface Creation: Fig. 3 illustrates the surface creation step in CAT for data obtained on scanners with different field strengths (1.5, 3.0, and 7.0 T esla).CA T uses a projection-based thickness method [ 19 ], which estimates the initial cortical thickness and initial central surface in a combined step, while handling partial volume information, sulcal blurring, and sulcal asymmetries, without explicit sulcus reconstruction.After this initial step, topological defects (i.e., anatomically incorrect connections between gyri or sulci) ar e r epair ed using spherical harmonics [ 20 ].The topological correction is follo w ed b y a surface r efinement, whic h r esults in the final central, pial, and white surface meshes.In the last step, the final pial and white matter surfaces are used to refine the initial cortical thickness estimate using the FreeSurfer thickness metric [ 22 , 23 ].Alternativ el y, the final central surface can be used to calculate metrics of cortical folding, as described under "Surface-based morphometry (SBM)." r Surface Registr ation: The r esulting individual centr al sur- faces are registered to the corresponding hemisphere of the FreeSurfer FsAverage template [ 28 ].During this process, the individual central surfaces are spherically inflated with minimal distortions [ 29 ], and a one-to-one mapping between the folding patterns of the individual and template spheres is created by a 2-dimensional (2D) version of the DARTEL a ppr oac h [ 21 , 30 ]. Figur e 3 D pr ovides information on the accur acy of CAT's surface r egistr ation.

Surf ace-based morphometr y (SBM)
SBM can be used to investigate cortical thickness or various parameters of cortical folding.The measurement of "cortical thickness" ca ptur es the width of the gray matter ribbon as the distance between its inner and outer boundary at thousands of points (see Fig. 4 ).To obtain measurements of "cortical folding," the user has a variety of options in CAT, ranging from Gyrification [ 35 ] to Sulcal Depth [ 36 ] to Cortical Complexity [ 37 ] to the Surface Ratio [ 38 ], as explained and illustrated in Fig. 4 .Similar to VBM, SBM incorporates a series of different steps: (i) surface creation and (ii) surface r egistr ation, as detailed abov e, and (iii) spatial smoothing.As a side note, since the measurements in native space ar e ma pped dir ectl y to the template during the spatial r egistr ation, no additional modulation (as in VBM) is needed to pr eserv e the individual differences.In contrast to VBM, SBM does not require brain size corrections because cortical thickness and cortical folding are not closely associated with total brain volume (unlike gray matter volume) [ 39 ].

Region-based processing and morphometry
In addition to voxel-or point-wise analyses via VBM or SBM, CAT provides an option to conduct regional analyses via region-based morphometry (RBM).For this purpose, the processing steps under voxel-based processing (surface-based processing, respectively) should be applied and follo w ed b y automatically calculating regional measurements .T his is ac hie v ed b y w orking with regions of interest (ROIs), defined using standardized atlases .T he r equir ed atlases are provided in CAT (see Supplementary Table S1 and Supplementary Table S2 ), but users can also work with their own atlases.
r Voxel-based R OIs: The v olumetric atlases a vailable in C AT have been defined on brain templates in MNI space and may be mapped to the individual brains by using the spatial registr ation par ameters determined during voxel-based pr ocessing.Volumetric measur es, suc h as r egional gr ay matter volume, can then be calculated for each ROI in native space.
r Surface-based ROIs: The surface atlases available in CAT are supplied on the FsAverage surface and can be mapped to the individual surfaces by using the spherical r egistr ation parameters determined during the surface-based processing.Surface-based measur es, suc h as cortical thic kness or cortical folding, are then calculated for each ROI in native space.

Performance of CAT
CAT allows pr ocessing str eams to be distributed to m ultiple pr ocessing cores, to reduce processing time.For example, CAT's analysis of 50 subjects (see "Example a pplication"), le v er a ging the inbuilt par allel pr ocessing ca pabilities on 4 cor es, r equir ed 7 hours of processing time when analyzing 1 image per subject (crosssectional stream) and 18 hours when processing 3 images per subject (longitudinal stream) for the entire sample.Application of all available w orkflo ws for a single T1-w eighted ima ge takes ar ound 35 minutes, as timed on an iMac with Intel Core i7 with 4 GHz and 32 GB RAM using MATLAB version 2017b, SPM12 version r7771, and CAT12.8 version r1945.
CAT's performance has been thor oughl y tested by e v aluating its accuracy , sensitivity , and robustness in comparison to other tools fr equentl y used in the neur oima ging comm unity.For this pur pose, we a pplied CAT and anal yzed r eal data (see "Example application") as well as simulated data generated from BrainWeb [ 40 ].The e v aluation pr ocedur es ar e detailed in Supplementary Note 1 and Supplementary Note 2 ; the outcomes are presented in Supplementary Fig. S1 and Supplementary Fig. S2 .C AT pro ved to be accur ate, sensitiv e, r eliable, and r obust, outperforming other common neur oima ging tools.

Longitudinal processing
Aside from offering a standard pipeline for cross-sectional analyses , C AT has specific longitudinal pipelines that ensure a local comparability both across subjects and across time points within subjects.Compared to the cross-sectional pipeline, these longitudinal pipelines render analysis outcomes more accurate when ma pping structur al c hanges ov er time.The user can choose between 3 different longitudinal pipelines: the first one for analyzing brain plasticity (o ver da ys , weeks , months), the second one for anal yzing br ain de v elopment (ov er months and years), and the third one for br ain a ging (ov er months , years , decades).For more details, refer to Supplementary Note 3 .

Quality control
CAT intr oduces a r etr ospectiv e quality contr ol fr ame work for the empirical quantification of essential image parameters, such as noise, intensity inhomogeneities, and image resolution (all of these can be impacted, for example, by motion artifacts).Separate par ameter-specific r atings ar e pr ovided as well as a handy ov er all r ating [ 41 ].Mor eov er, ima ge outliers can be easily identified, either dir ectl y based on the afor ementioned indicators of the ima ge quality or by calculating a z -score determined by the quality of the ima ge pr ocessing as well as by the anatomical c har acteristics of eac h br ain.For mor e details, r efer to Supplementary Note 4 .

Mapping onto the cortical surface
CAT allows the user to map voxel-based values (e .g., quantitative , functional, or diffusion parameters) to individual brain surfaces (i.e., pial, central, and/or white matter) for surface-based analyses .T he integrated equi-volume model [ 42 ] also considers the shift of c ytoar chitectonic lay ers caused b y the local folding.Op-tionall y, CAT also allows ma pping of voxel v alues at m ultiple positions along the surface normal at each node-supporting a layerspecific analysis of ultra-high resolution functional MRI data [ 43 , 44 ].For more details, refer to Supplementary Note 5 .

T hr eshold-fr ee cluster enhancement (TFCE)
CAT comes with its own thr eshold-fr ee cluster enhancement (TFCE) toolbox and provides the option to apply TFCE [ 45 ] in any statistical second-level analysis in SPM, for both voxel-based and surface-based analyses.It can also be emplo y ed to analyze functional MRI (fMRI) or diffusion tensor imaging (DTI) data.A particularl y helpful featur e of the TFCE toolbox is that it automatically r ecognizes exc hangeability bloc ks and potential nuisance par ameters [ 46 ] from an existing statistical design in SPM.For more details, refer to Supplementary Note 4 .

Visualization
CAT allows a user to generate graphs and images, which creates a solid basis to explore findings as well as to generate ready-topublish figures according to prevailing standards.More specifically, it includes 2 distinct sets of tools to visualize results: the first set pr epar es both voxel-and surface-based data for visualization by providing options for thresholding the default SPM T -maps or F -maps and for converting statistical parameters (e.g., T -maps and F -maps into p -maps).The second set of tools visualizes the data offering the user ample options to select from different brain templates , views , slices , significance parameters , significance thresholds , color schemes , and so on (see Fig. 5 ).

Example application
To demonstrate an application of C AT, we in vestigated an actual dataset focusing on the effects of Alzheimer's disease on brain structur e. Mor e specificall y, we set out to compar e 25 patients with Alzheimer's disease and 25 matched controls.We applied (i) a VBM analysis focusing on voxel-wise gray matter volume, (ii) an RBM analysis focusing on regional gray matter volume (i.e., a v oxel-based R OI anal ysis), (iii) a surface-based anal ysis focusing on point-wise cortical thickness, and (iv) an RBM analysis focusing on regional cortical thickness (i.e., a surface-based ROI analysis).Given the wealth of liter atur e on Alzheimer's disease, we expected atrophy in gray matter volume and cortical thickness in patients compared to controls, particularly in regions around the medial temporal lobe and the default mode network [ 47 , 48 ].In addition to distinguishing between the 4 morphological measures (i-iv), all analyses were conducted using both cross-sectional and longitudinal streams in CAT.Overall, we expected that longitudinal changes would manifest in similar brain regions to crosssectional group differences but that cross-sectional effects would be more pronounced than longitudinal effects .T he outcomes of this example analysis are presented and discussed in the next section.

Example application
As shown in Fig. 6 , all 4 cross-sectional streams-investigating voxel-based gray matter volume, regional gray matter volume , point-wise thickness , and r egional thic kness-r e v ealed widespr ead gr oup differ ences between patients with Alzheimer's disease (AD) and matched controls.Overall, the effects were compar able between cr oss-sectional and longitudinal str eams, but the Downloaded from https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520 by B Pool-Zobel user on 07 August 2024 Mor e specificall y, using VBM, significantl y smaller voxel-wise gray matter volumes were observed in patients with AD compared to contr ols, particularl y in the medial and later al tempor al lobes and within regions of the default mode network (Fig. 6 A, top).Similarly, the longitudinal follow-up revealed a significantly stronger gray matter volume loss in patients compared to controls, with effects located in the medial temporal lobe as well as the default mode network (Fig. 6 A, bottom).The voxel-based ROI analysis resulted in a significance pattern similar to the VBM study, with particularl y pr onounced gr oup differ ences in the tempor al lobe that extended into additional brain areas, including those comprising the default mode network (Fig. 6 B, top).Again, the longitudinal analysis yielded similar but less pronounced findings than the cross-sectional analysis, although longitudinal effects were stronger than in the VBM analysis (Fig. 6 B, bottom).
Using SBM, the point-wise cortical thickness analysis yielded a pattern similar to the VBM analysis with significantly thinner cortices in patients, particularly in the medial and lateral temporal lobe and within regions of the default mode network (Fig. 6 C, top).Just as in the VBM analysis, significant clusters were widespread and r eac hed far into adjacent r egions.Again, the r esults fr om the longitudinal stream were less widespread and significant than the r esults fr om the cr oss-sectional str eam (Fig. 6 C, bottom).Finally, the surface-based ROI analysis largely replicated the local findings from the SBM analysis (Fig. 6

D, top/bottom).
Ov er all, the r esults of all anal ysis str eams corr obor ate prior findings in the Alzheimer's disease liter atur e, particularl y the strong disease effects within the medial temporal lobe and regions of the default mode network [ 47 , 48 ].Furthermore, the compara-ble pattern across measures suggests a considerable consistency between av ailable mor phometric options, e v en if gr ay matter volume and cortical thickness are biologically different and not perfectl y r elated [ 49 , 50 ].

Ev alua tion of CAT12
As shown in Supplementary Fig. S1 and Supplementary Fig. S2 , C AT12 pro ved to be accurate , sensitive , reliable , and robust, outperforming other common neur oima ging tools.Similar conclusions have been drawn in independent evaluations testing 1 or mor e softwar e in comparison with C AT12.For example , Guo et al. [ 51 ] e v aluated the r epeatability and r epr oducibility of br ain volume measurements using F reeSurfer, FSL-SIEN AX, and SPM and highlighted the reliability of C AT12.Similarly, C AT12 emerged as a robust option when demonstrating that the choice of the processing pipeline influences the location of neuroanatomical brain markers [ 52 ].Last but not least, Khlif et al. [ 53 ] compared the outcomes of CAT12's automated segmentation of the hippocampus with those ac hie v ed based on manual tracing and demonstrated that both approaches produced comparable hippocampal volume.
In ad dition, n umer ous e v aluations suggest that CA T12 performs at least as well as other common neur oima ging tools and, as such, offers a valuable alternative.For example, Tav ar es et al. [ 54 ] conducted a VBM study and concluded that the segmentation pipelines implemented in CAT12 and SPM12 pr ovided r esults that ar e highl y corr elated and that the c hoice of the pipeline had no impact on the accuracy of any brain volume measure.Along the same lines, but for SBM, Ay et al. [ 55 ] reported that CAT12 and Fr eeSurfer pr oduced equall y v alid r esults for parcel-based cor-Downloaded from https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520 by B Pool-Zobel user on 07 August 2024 Voxel-based morphometry (VBM) findings: Results were estimated using threshold-free cluster enhancement (TFCE), corrected for multiple comparisons by controlling the family-wise error (FWE), and thresholded at P < 0.001 for cross-sectional data and P < 0.05 for longitudinal data.Significant findings were projected onto orthogonal sections intersecting at (x = −27 mm, y = −10 mm, z = −19 mm) of the mean brain created from the entire study sample ( n = 50).(B) Volumetric regions of interest (ROI) findings: ROIs were defined using the Neuromorphometrics atlas.Results were corrected for multiple comparisons by controlling the false discovery rate (FDR) and thresholded at q < 0.001 for cross-sectional data and q < 0.05 for longitudinal data.Significant findings were projected onto the same orthogonal sections as for the VBM findings.(C) Surface-based morphometry (SBM) findings: Results were estimated using TFCE, FWE-corrected, and thresholded at P < 0.001 for cross-sectional data and P < 0.05 for longitudinal data.Significant findings wer e pr ojected onto the Fr eeSurfer FsAverage surface.(D) Surface R OI findings: R OIs w ere defined using the DK40 atlas.Results w er e FDR-corr ected and thresholded at q < 0.001 for cross-sectional data and q < 0.05 for longitudinal data.Significant findings were projected onto the FsAverage surface.tical thickness calculations.de Fátima Machado Dias et al. [ 56 ] addressed the issue of reproducibility and observed that cortical thic kness measur es using CAT12 and Fr eeSurfer wer e compar able at the individual le v el.Mor eov er, Seiger et al. [ 57 ] conducted a study in patients with Alzheimer's disease and healthy contr ols, in whic h CAT12 and Fr eeSurfer pr ovided consistent cortical thickness estimates and excellent test-retest variability scores.Velázquez et al. [ 58 ] supported these findings when comparing CAT12 and FreeSurfer with 3 voxel-based methods in a testr etest anal ysis and clinical a pplication.Finall y, Righart et al. [ 59 ] compared volume and surface-based cortical thickness measurements in multiple sclerosis and emphasized CAT12's consistent performance.These collective findings from multiple studies support the notion that CAT is a robust and reliable tool for both VBM and SBM anal yses, pr oducing r esults that ar e compar able to and, in some cases, superior to other established neur oima ging softwar e.

Conclusion
CAT is suitable for desktop and laptop computers as well as highperformance clusters.It is fully integrated into the SPM environment within MATLAB but also allows process execution dir ectl y from the command shell, without having to start SPM.CAT can also run without a MATLAB license by using the stand-alone version or by using Octave instead of MATLAB.In terms of performance , C AT allows for ultra-fast processing and analysis and also Downloaded from https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520 by B Pool-Zobel user on 07 August 2024 is more sensitive in detecting significant effects compared to other common tools used by the neur oima ging comm unity.Mor eov er, it better handles varying levels of noise and signal inhomogeneities.Furthermore , C AT is easy to integrate with non-SPM software pac ka ges and also supports the Br ain Ima ging Data Structur e (BIDS) standards [ 60 ].Ther efor e, CAT is ideall y suited to process not only small datasets (as demonstrated in the example application) but also big datasets, such as samples of the UK Biobank [ 61 ] or ENIGMA [ 62 ].Finally, while CAT is currently targeted at structur al ima ging data, some features (e.g., high-dimensional spatial r egistr ation or ma pping onto the cortical surface) may also be used for the analysis of functional, diffusion, or quantitative MRI or EEG/MEG data.

Application example
Data source Data for the application example were obtained from the Alzheimer's Disease Neur oima ging Initiativ e (ADNI) database [ 63 ].The ADNI ( RRID:SCR _ 003007 ) was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD.The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neur opsyc hological assessment can be combined to measure the pr ogr ession of mild cognitive impairment (MCI) and early AD.For up-to-date information, see [ 64 ].

Sample characteristics
For the purpose of the current study, we compiled a sample of 50 subjects with 3D T1-weighted structural brain images from the ADNI database.Specificall y, we r andoml y selected the first 25 subjects (16 24).Informed consent was obtained from all r esearc h participants.All subjects had br ain scans at baseline (first scan at enrollment) and at 2 follow-up visits, at 1 year and 2 years after the first scan.All brain images were acquired on 1.5 Tesla scanners (Siemens, General Electric, Philips) using a 3D T1weighted sequence with an in-plane resolution between 0.94 and 1.25 mm and a slice thickness of 1.2 mm.

Data processing
All T1-weighted data were processed using CAT12 following the cross-sectional (or longitudinal, respectively) processing stream for VBM, SBM (cortical thickness), and ROI analyses (see Fig. 2 ) according to the descriptions provided under "Computational morphometry."For each subject, only their first time point was included in the cross-sectional stream, whereas all 3 time points were included in the longitudinal stream.The processing streams for the VBM analysis resulted in modulated and registered gray matter segments, whic h wer e smoothed using a 6-mm Gaussian kernel.The ima ge-pr ocessing str eams for the SBM anal ysis r esulted in the r egister ed point-wise cortical thickness measures, whic h wer e smoothed using a 12-mm Gaussian k ernel.The vo xelbased ROI analysis used the Neuromorphometrics atlas ( RRID: SCR _ 005656 ) [ 65 ] to calculate the regional gray matter volumes; the surface-based ROI analysis employed the DK40 atlas [ 34 ] to calculate regional cortical thickness.

Statistical analysis
For each variable of interest-voxel-wise gray matter volume, regional gray matter volume, point-wise cortical thickness, and regional cortical thickness-the dependent measures (e.g., the registered, modulated, and smoothed gray matter segments for voxelwise gray matter) were entered into the statistical model.For the cr oss-sectional str eam, group (patients with AD vs. contr ols) was defined as the independent variable.For the longitudinal stream, the interaction between group and time was defined as the independent v ariable, wher eas subject was defined as a v ariable of no interest.For the VBM and the v oxel-based R OI analyses, data w ere corrected for TIV using "global scaling" (because TIV correlated with group , the effect of interest).Since cortical thickness does not scale with brain size [ 39 ], no corrections for TIV were applied for the SBM and the surface-based ROI analyses.For the crosssectional analysis, we additionally included age as a nuisance parameter.
For the VBM and SBM anal yses, r esults wer e corr ected for multiple comparisons by applying TFCE [ 45 ] and controlling the famil y-wise err or at P ≤ 0.001 (cross-sectional) and P ≤ 0.05 (longitudinal).For the voxel-based and surface-based ROI anal yses, r esults wer e corr ected for m ultiple comparisons by contr olling the false discov ery r ate [ 66 ] at q ≤ 0.001 (cross-sectional) and q ≤ 0.05 (longitudinal).All statistical tests were 1-tailed given our a priori hypothesis that patients with AD have less gray matter at baseline and a larger loss of gray matter over time.
The outcomes of the VBM and voxel-based ROI analyses were overlaid onto orthogonal sections of the av er a ge br ain that was cr eated fr om the spatiall y r egister ed T1-weighted ima ges of the study sample ( n = 50); the outcomes of the SBM and surface-based ROI analyses were projected onto the FsAverage surface.

Figure 2 :
Figure2: Main processing streams.(A) Simplified pipeline: image processing in CAT can be separated into a mandatory voxel-based processing stream and an optional subsequent surface-based processing stream.Each stream requires different templates and atlases and, in addition, tissue probability maps for the voxel-based stream.The voxel-based stream consists of 2 main modules-for tissue segmentation and spatial registration-resulting in spatiall y r egister ed (and modulated) gr a y matter/white matter segments , whic h pr o vides the basis for voxel-based morphometry (VBM).T he surface-based stream also consists of 2 main modules-for surface creation and registration-resulting in spatially registered surface maps, which provide the basis for surface-based morphometry (SBM).Both streams also include an optional module each to analyze regions of interest (ROIs) resulting in ROI-specific mean volumes (mean surface v alues, r espectiv el y).This pr ovides the basis for region-based morphometry (RBM).(B) Detailed pipeline: to illustrate the differences from SPM, the CAT pipeline is detailed with its individual processing steps .T he SPM methods used are shown in blue and italic font: images are first denoised by a spatially adaptive nonlocal means (SANLM) filter[ 15 ] and resampled to an isotropic voxel size.After applying an initial bias correction to facilitate the affine registration, SPM's unified segmentation[ 16 ] is used for the skull stripping and as a starting estimate for the ada ptiv e maxim um a posteriori (AMAP) segmentation[ 17 ] with partial volume estimation (PVE)[ 18 ].In addition, SPM's segmentation is used to locall y corr ect ima ge intensities.Finall y, the outcomes of the AMAP segmentation ar e r egister ed to the MNI template using SPM's shooting r egistr ation.The outcomes of the AMAP segmentation are also used to estimate cortical thickness and the central surface using a projection-based thickness (PBT) method[ 19 ].More specifically, after repairing topology defects[ 20 ], central, pial, and white matter surface meshes are generated.The individual left and right central surfaces are then registered to the corresponding hemisphere of the FreeSurfer template using a 2D version of the DARTEL approach[ 21 ].In the final step, the pial and white matter surfaces are used to refine the initial cortical thickness estimate using the FreeSurfer thickness metric[ 22 , 23 ].

Figure 3 :
Figure3: Evaluation of segmentation and registration accuracy.(A) Segmentation Accuracy: Most a ppr oac hes for brain segmentation assume that each voxel belongs to a particular tissue class, such as gray matter (GM), white matter (WM), or cerebrospinal fluid (CSF).Ho w e v er, the spatial resolution of brain images is limited, leading to so-called partial volume effects (PVE) in voxels containing a mixture of different tissue types, such as GM/WM and GM/CSF.As PVE a ppr oac hes ar e highl y susceptible to noise, we combined the PVE model[ 18 ] with a spatial ada ptiv e nonlocal means denoising filter[ 15 ].To validate our method, we used a gr ound-truth ima ge fr om the Br ainWeb[ 31 ] database with varying noise le v els of 1-9%.The segmentation accur acy for all tissue types (GM, WM, CSF) was determined by calculating a kappa coefficient (a kappa coefficient of 1 means that there is perfect correspondence between the segmentation result and the ground truth).Left panel: The effect of the PVE model and the denoising filter on the tissue segmentation at the extremes of 1% and 9% noise.Right panel: The kappa coefficient over the range of different noise levels.Both panels demonstrate the advantage of combining the PVE model with a spatial ada ptiv e nonlocal means denoising filter, with particularly strong benefits for noisy data.(B) Registration Accuracy: To ensure an appropriate overlap of corresponding anatomical regions across brains, high-dimensional nonlinear spatial registration is r equir ed.CAT uses a sophisticated shooting a ppr oac h[ 24 ], together with an av er a ge template created from the IXI dataset[ 32 ].The figure shows the impr ov ed accur acy (i.e., a mor e detailed av er a ge ima ge) when spatiall y r egistering 555 br ains using the so-called shooting r egistr ation and the Dartel r egistr ation compar ed to the SPM standard r egistr ation.(C) Preprocessing Accuracy: We v alidated the performance of region-based morphometry (RBM) in CAT by comparing measures derived from automatically extracted regions of interest (ROI) versus manually labeled ROIs.For the voxel-based analysis, we used 56 structur es, manuall y labeled in 40 brains that provided the basis for the LPBA40 atlas[ 33 ].The gray matter volumes from those manually labeled regions served as the ground truth against which the gray matter volumes calculated using CAT and the LPBA40 atlas were then compared.For the surface-based analysis, we used 34 structures that were manually labeled in 39 brains according to Desikan et al.[ 34 ].The mean cortical thic kness fr om those manuall y labeled r egions serv ed as the gr ound truth a gainst whic h the mean cortical thic kness calculated using CAT and the Desikan atlas were compared.The diagrams show excellent overlap between manually and automatically labeled regions in both voxel-based (left) and surface-based (right) analyses.(D) Consistency of Segmentation and Surface Creation: Data from the same brain were acquired on MRI scanners with differ ent isotr opic spatial r esolutions and differ ent field str engths: 1.5T MPRA GE with a 1-mm voxel size, 3T MPRA GE with a 0.8-mm voxel size, and 7T MP2RAGE with a 0.7-mm voxel size.Section Views: The left hemispheres depict the central ( green ), pial ( blue ), and white matter ( red ) surfaces; the right hemispheres show the gray matter segments.Rendered Views: The color bar encodes point-wise cortical thickness projected onto the left hemisphere central surface.Both section views and hemisphere renderings demonstrate the consistency of the outcomes of the segmentation and surface cr eation pr ocedur es acr oss differ ent spatial r esolutions and field str engths.

Figure 5 :
Figure 5: Examples of CAT's visualization of results.Both surface-and voxel-based data can be presented on surfaces such as (A) the (inflated) FsAverage surface or (B) the flatmap of the Connectome W orkbench.V olumetric maps can also be displayed as (C) slice overlays on the MNI av er a ge brain or (D) a maximum intensity projection (so-called glass brains).All panels show the corrected P values from the longitudinal VBM study in our example (see "Example application").

Figure 6 :
Figure 6: Pronounced atrophy in gray matter and cortical thickness in patients with Alzheimer's disease compared to healthy control subjects.(A)Voxel-based morphometry (VBM) findings: Results were estimated using threshold-free cluster enhancement (TFCE), corrected for multiple comparisons by controlling the family-wise error (FWE), and thresholded at P < 0.001 for cross-sectional data and P < 0.05 for longitudinal data.Significant findings were projected onto orthogonal sections intersecting at (x = −27 mm, y = −10 mm, z = −19 mm) of the mean brain created from the entire study sample ( n = 50).(B) Volumetric regions of interest (ROI) findings: ROIs were defined using the Neuromorphometrics atlas.Results were corrected for multiple comparisons by controlling the false discovery rate (FDR) and thresholded at q < 0.001 for cross-sectional data and q < 0.05 for longitudinal data.Significant findings were projected onto the same orthogonal sections as for the VBM findings.(C) Surface-based morphometry (SBM) findings: Results were estimated using TFCE, FWE-corrected, and thresholded at P < 0.001 for cross-sectional data and P < 0.05 for longitudinal data.Significant findings wer e pr ojected onto the Fr eeSurfer FsAverage surface.(D) Surface R OI findings: R OIs w ere defined using the DK40 atlas.Results w er e FDR-corr ected and thresholded at q < 0.001 for cross-sectional data and q < 0.05 for longitudinal data.Significant findings were projected onto the FsAverage surface.